Examining the role of physician areas of practice on the gender pay gap in family medicine in Ontario
Bibliographic record
Abstract
Context Previous research has shown a pay gap between male and female family physicians (FPs) in Ontario. Physician payment models can explain part of the gap, but a lot remains unknown about what factors contribute to the gap and how to reduce it. Another factor that impacts FPs total payments is the area of practice. It is estimated that over 30% of FPs in Ontario provide care in an area of practice other than primary care, some of which are known to have compensation levels much higher than primary care. Objective The objective of this study is to determine how much of the gender pay gap among FPs is explained by the physician areas of practice. Study design We analyzed the gender pay gap among FPs, while accounting for previously observed factors that contribute to it. These previously studied variables included physician activity and practice factors such as: years of practice, part-time status, work setting, work after-hours, and physician sex. The areas of practice were identified based on the billing activity and incorporated into our analysis, which changed the estimated effect of physician sex on total payments. We also stratified the analysis by physician payment model: FFS, enhanced-FFS, and capitation. Data Set Family physician billing and payment data provided by the Ontario Ministry of Health for April 1, 2022, to March 31, 2023. Population Studied All FPs who submitted billings to the Ontario Health Insurance Plan between April 2022 and March 2023. Intervention/Instrument Inclusion of FPs areas of practice in multivariable linear regressions. Outcome measures Family physician average daily payments, by sex, areas of practice, and predominant payment model. Results The inclusion of physician areas of practice reduced the unexplained gender pay gap from 16.5 (95% CI: 14.2 - 18.7) to 13.3 percent (11.1 - 15.5). The effect was observed on all the stratified payment models, changing from 21.6 (16.7 - 26.4) to 15.7% (11.1 - 20.3) for FFS; from 19.3 (14.8 - 23.8) to 16.7 (12.3 - 21.0) on enhanced FFS; and from 12.0 (10.0 - 14.0) to 10.8% (8.8 - 12.8) among FPs in capitation plans. Conclusion The reduction of the unexplained gender pay gap suggests that areas of practice where women are underrepresented tend to be more highly remunerated. Addressing the pay gap between areas of practice can help lowering the gender pay gap, but other unexplained gender-related differences contributing to the gender pay gap persist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".